{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 机器学习模型作业"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "作业内容：使用已完成特征工程的Home Credit客户贷款数据集（train_after_feature_engineering.csv和test_after_feature_engineering.csv），在随机森林baseline模型的基础上做超参调优。\n",
    "\n",
    "注意：数据读取、归一化和baseline模型训练的代码都已给出，不必改动，需要你填写的代码在随机森林超参调优部分。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据读取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读取训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307511, 295)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv('train_after_feature_engineering.csv')\n",
    "df_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读取测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <th>REGION_POPULATION_RELATIVE</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>DAYS_REGISTRATION</th>\n",
       "      <th>...</th>\n",
       "      <th>WALLSMATERIAL_MODE_Mixed</th>\n",
       "      <th>WALLSMATERIAL_MODE_Monolithic</th>\n",
       "      <th>WALLSMATERIAL_MODE_NaN</th>\n",
       "      <th>WALLSMATERIAL_MODE_Others</th>\n",
       "      <th>WALLSMATERIAL_MODE_Panel</th>\n",
       "      <th>WALLSMATERIAL_MODE_Stone, brick</th>\n",
       "      <th>WALLSMATERIAL_MODE_Wooden</th>\n",
       "      <th>EMERGENCYSTATE_MODE_NaN</th>\n",
       "      <th>EMERGENCYSTATE_MODE_No</th>\n",
       "      <th>EMERGENCYSTATE_MODE_Yes</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48744 rows × 294 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SK_ID_CURR  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0          100001             0          135000.0    568800.0      20560.5   \n",
       "1          100005             0           99000.0    222768.0      17370.0   \n",
       "2          100013             0          202500.0    663264.0      69777.0   \n",
       "3          100028             2          315000.0   1575000.0      49018.5   \n",
       "4          100038             1          180000.0    625500.0      32067.0   \n",
       "...           ...           ...               ...         ...          ...   \n",
       "48739      456221             0          121500.0    412560.0      17473.5   \n",
       "48740      456222             2          157500.0    622413.0      31909.5   \n",
       "48741      456223             1          202500.0    315000.0      33205.5   \n",
       "48742      456224             0          225000.0    450000.0      25128.0   \n",
       "48743      456250             0          135000.0    312768.0      24709.5   \n",
       "\n",
       "       AMT_GOODS_PRICE  REGION_POPULATION_RELATIVE  DAYS_BIRTH  DAYS_EMPLOYED  \\\n",
       "0             450000.0                    0.018850      -19241        -2329.0   \n",
       "1             180000.0                    0.035792      -18064        -4469.0   \n",
       "2             630000.0                    0.019101      -20038        -4458.0   \n",
       "3            1575000.0                    0.026392      -13976        -1866.0   \n",
       "4             625500.0                    0.010032      -13040        -2191.0   \n",
       "...                ...                         ...         ...            ...   \n",
       "48739         270000.0                    0.002042      -19970        -5169.0   \n",
       "48740         495000.0                    0.035792      -11186        -1149.0   \n",
       "48741         315000.0                    0.026392      -15922        -3037.0   \n",
       "48742         450000.0                    0.018850      -13968        -2731.0   \n",
       "48743         270000.0                    0.006629      -13962         -633.0   \n",
       "\n",
       "       DAYS_REGISTRATION  ...  WALLSMATERIAL_MODE_Mixed  \\\n",
       "0                -5170.0  ...                         0   \n",
       "1                -9118.0  ...                         0   \n",
       "2                -2175.0  ...                         0   \n",
       "3                -2000.0  ...                         0   \n",
       "4                -4000.0  ...                         0   \n",
       "...                  ...  ...                       ...   \n",
       "48739            -9094.0  ...                         0   \n",
       "48740            -3015.0  ...                         0   \n",
       "48741            -2681.0  ...                         0   \n",
       "48742            -1461.0  ...                         0   \n",
       "48743            -1072.0  ...                         0   \n",
       "\n",
       "       WALLSMATERIAL_MODE_Monolithic  WALLSMATERIAL_MODE_NaN  \\\n",
       "0                                  0                       0   \n",
       "1                                  0                       1   \n",
       "2                                  0                       1   \n",
       "3                                  0                       0   \n",
       "4                                  0                       1   \n",
       "...                              ...                     ...   \n",
       "48739                              0                       1   \n",
       "48740                              0                       1   \n",
       "48741                              0                       0   \n",
       "48742                              0                       0   \n",
       "48743                              0                       1   \n",
       "\n",
       "       WALLSMATERIAL_MODE_Others  WALLSMATERIAL_MODE_Panel  \\\n",
       "0                              0                         0   \n",
       "1                              0                         0   \n",
       "2                              0                         0   \n",
       "3                              0                         1   \n",
       "4                              0                         0   \n",
       "...                          ...                       ...   \n",
       "48739                          0                         0   \n",
       "48740                          0                         0   \n",
       "48741                          0                         0   \n",
       "48742                          0                         1   \n",
       "48743                          0                         0   \n",
       "\n",
       "       WALLSMATERIAL_MODE_Stone, brick  WALLSMATERIAL_MODE_Wooden  \\\n",
       "0                                    1                          0   \n",
       "1                                    0                          0   \n",
       "2                                    0                          0   \n",
       "3                                    0                          0   \n",
       "4                                    0                          0   \n",
       "...                                ...                        ...   \n",
       "48739                                0                          0   \n",
       "48740                                0                          0   \n",
       "48741                                1                          0   \n",
       "48742                                0                          0   \n",
       "48743                                0                          0   \n",
       "\n",
       "       EMERGENCYSTATE_MODE_NaN  EMERGENCYSTATE_MODE_No  \\\n",
       "0                            0                       1   \n",
       "1                            1                       0   \n",
       "2                            1                       0   \n",
       "3                            0                       1   \n",
       "4                            1                       0   \n",
       "...                        ...                     ...   \n",
       "48739                        1                       0   \n",
       "48740                        1                       0   \n",
       "48741                        0                       1   \n",
       "48742                        0                       1   \n",
       "48743                        1                       0   \n",
       "\n",
       "       EMERGENCYSTATE_MODE_Yes  \n",
       "0                            0  \n",
       "1                            0  \n",
       "2                            0  \n",
       "3                            0  \n",
       "4                            0  \n",
       "...                        ...  \n",
       "48739                        0  \n",
       "48740                        0  \n",
       "48741                        0  \n",
       "48742                        0  \n",
       "48743                        0  \n",
       "\n",
       "[48744 rows x 294 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv('test_after_feature_engineering.csv')\n",
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分离训练集的特征和标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = df_train['TARGET']\n",
    "feats_train = df_train.drop(['TARGET'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(feats_train)\n",
    "feats_train = scaler.transform(feats_train)\n",
    "feats_test = scaler.transform(df_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 随机森林Baseline模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 100building tree 2 of 100building tree 3 of 100\n",
      "\n",
      "\n",
      "building tree 4 of 100building tree 5 of 100\n",
      "building tree 6 of 100\n",
      "building tree 7 of 100building tree 8 of 100\n",
      "\n",
      "\n",
      "building tree 9 of 100\n",
      "building tree 10 of 100\n",
      "building tree 11 of 100\n",
      "building tree 12 of 100\n",
      "building tree 13 of 100\n",
      "building tree 14 of 100\n",
      "building tree 15 of 100\n",
      "building tree 16 of 100\n",
      "building tree 17 of 100\n",
      "building tree 18 of 100\n",
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      "\n",
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     ]
    },
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     "output_type": "stream",
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      "building tree 41 of 100\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed:    5.5s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# 设置参数\n",
    "params = {\n",
    "    'criterion': 'entropy',\n",
    "    'n_estimators': 100,\n",
    "    'max_depth': 100,\n",
    "    'min_samples_leaf': 1000,\n",
    "    'max_features': 100,\n",
    "    'max_samples': 0.1,\n",
    "    'bootstrap': True,\n",
    "    'oob_score': True,\n",
    "    'class_weight': \"balanced\",\n",
    "    'n_jobs': 8,\n",
    "    'random_state': 33,\n",
    "    'verbose': 2,\n",
    "}\n",
    "\n",
    "# 创建模型\n",
    "rf_model = RandomForestClassifier(**params)\n",
    "\n",
    "# 模型训练\n",
    "rf_model.fit(feats_train, train_labels)\n",
    "\n",
    "# 模型对测试集做预测\n",
    "y_pred_baseline = rf_model.predict_proba(feats_test)[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建提交结果格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "submit = df_test[['SK_ID_CURR']]\n",
    "submit['TARGET'] = y_pred_baseline\n",
    "\n",
    "submit.to_csv('rf_baseline.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提交的得分大概为0.7190。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 随机森林超参调优"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "随机森林需要调优的超参包括：\n",
    "\n",
    "1. **n_estimators**：树的数目\n",
    "\n",
    "\n",
    "2. **max_features**：最大特征数目\n",
    "\n",
    "\n",
    "3. **max_depth**：树的深度\n",
    "\n",
    "\n",
    "4. **min_samples_leaf**：叶子节点的最小样本数\n",
    "\n",
    "随机森林的调参不需要用到交叉验证，这是因为每个基学习器只会抽取一部分样本，其余的样本（包外样本）可以作为校验集，从而不必显式进行交叉验证，只要设置参数oob_score=True即可，包外样本的评价结果（正确率）可以调用oob_score_查看。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面给出了n_estimators的调优代码，各位学员可以参照这部分的代码对其他超参做调优，相应部分也提示了这些超参的大致范围。\n",
    "\n",
    "作业要求：调参后的随机森林模型提交kaggle的得分不低于0.723。\n",
    "\n",
    "提交网址：https://www.kaggle.com/c/home-credit-default-risk/submit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### n_estimators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "n_estimators调参建议范围：50-300。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 50\n",
      "building tree 2 of 50building tree 3 of 50\n",
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      "\n",
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    4.7s\n"
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      "building tree 33 of 50\n",
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      "building tree 33 of 60\n",
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      "\n",
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      "building tree 1 of 80\n",
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      "building tree 1 of 100\n",
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      "\n",
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      "[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed:   15.9s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
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     "text": [
      "building tree 1 of 110\n",
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    4.7s\n"
     ]
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      "building tree 33 of 110building tree 34 of 110\n",
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    },
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     "output_type": "stream",
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      "[Parallel(n_jobs=8)]: Done 110 out of 110 | elapsed:   17.2s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "building tree 1 of 120\n",
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    },
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      "building tree 36 of 120\n",
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    },
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      "[Parallel(n_jobs=8)]: Done 120 out of 120 | elapsed:   19.2s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
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     "output_type": "stream",
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      "building tree 1 of 130\n",
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    5.2s\n"
     ]
    },
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      "building tree 36 of 130\n",
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    },
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      "[Parallel(n_jobs=8)]: Done 130 out of 130 | elapsed:   21.7s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
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     "text": [
      "building tree 1 of 140building tree 2 of 140\n",
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    },
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      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
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      "building tree 1 of 150\n",
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      "\n",
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    },
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      "building tree 38 of 150\n",
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    },
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      "[Parallel(n_jobs=8)]: Done 150 out of 150 | elapsed:   23.1s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "building tree 1 of 160\n",
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    },
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    4.6s\n"
     ]
    },
    {
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     "output_type": "stream",
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      "building tree 36 of 160\n",
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    },
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      "[Parallel(n_jobs=8)]: Done 160 out of 160 | elapsed:   24.6s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "building tree 1 of 170\n",
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    },
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    4.7s\n"
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    },
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     "output_type": "stream",
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      "building tree 33 of 170\n",
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    },
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      "[Parallel(n_jobs=8)]: Done 146 tasks      | elapsed:   23.0s\n"
     ]
    },
    {
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     "output_type": "stream",
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      "building tree 158 of 170\n",
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      "[Parallel(n_jobs=8)]: Done 170 out of 170 | elapsed:   26.1s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
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     "output_type": "stream",
     "text": [
      "building tree 1 of 180\n",
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    4.7s\n"
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      "building tree 33 of 180\n",
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      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
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      "building tree 1 of 190\n",
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      "[Parallel(n_jobs=8)]: Done 190 out of 190 | elapsed:   29.0s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
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      "building tree 1 of 200\n",
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      "\n",
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      "building tree 36 of 200\n",
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      "[Parallel(n_jobs=8)]: Done 200 out of 200 | elapsed:   30.8s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
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      "building tree 1 of 210\n",
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      "[Parallel(n_jobs=8)]: Done 220 out of 220 | elapsed:   33.6s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
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      "[Parallel(n_jobs=8)]: Done 230 out of 230 | elapsed:   36.1s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
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    },
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      "[Parallel(n_jobs=8)]: Done 240 out of 240 | elapsed:   37.3s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
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      "building tree 1 of 250\n",
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      "[Parallel(n_jobs=8)]: Done 250 out of 250 | elapsed:   38.9s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
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     "output_type": "stream",
     "text": [
      "building tree 1 of 260\n",
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    4.8s\n"
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      "building tree 36 of 260\n",
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      "[Parallel(n_jobs=8)]: Done 146 tasks      | elapsed:   23.4s\n"
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      "[Parallel(n_jobs=8)]: Done 260 out of 260 | elapsed:   40.4s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
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     "output_type": "stream",
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      "building tree 1 of 270\n",
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    4.6s\n"
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      "[Parallel(n_jobs=8)]: Done 146 tasks      | elapsed:   23.1s\n"
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    },
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      "[Parallel(n_jobs=8)]: Done 270 out of 270 | elapsed:   42.2s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
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     "output_type": "stream",
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      "building tree 1 of 280\n",
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    4.8s\n"
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      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   44.5s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
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      "building tree 1 of 290\n",
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      "\n",
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      "\n",
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      "[Parallel(n_jobs=8)]: Done 146 tasks      | elapsed:   24.0s\n"
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    },
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      "building tree 156 of 290\n",
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      "\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Done 290 out of 290 | elapsed:   46.1s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
    {
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     "output_type": "stream",
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      "building tree 1 of 300\n",
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      "\n",
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      "\n",
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      "\n",
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    5.0s\n"
     ]
    },
    {
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      "[Parallel(n_jobs=8)]: Done 146 tasks      | elapsed:   24.8s\n"
     ]
    },
    {
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      "building tree 159 of 300\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Done 300 out of 300 | elapsed:   47.7s finished\n"
     ]
    }
   ],
   "source": [
    "# 需要调优的参数\n",
    "tuned_n_estimators = range(50, 301, 10)\n",
    "results = []\n",
    "\n",
    "# 设置参数\n",
    "params = {\n",
    "    'criterion': 'entropy',\n",
    "    'max_depth': 100,\n",
    "    'max_features': 100,\n",
    "    'max_samples': 0.1,\n",
    "    'bootstrap': True,\n",
    "    'oob_score': True,\n",
    "    'class_weight': 'balanced',\n",
    "    'n_jobs': 8,\n",
    "    'random_state': 33,\n",
    "    'verbose': 2,\n",
    "}\n",
    "\n",
    "# 模型循环调参\n",
    "for n_estimators in tuned_n_estimators:\n",
    "    params['n_estimators'] = n_estimators\n",
    "    rf_model = RandomForestClassifier(**params)\n",
    "    rf_model.fit(feats_train, train_labels)\n",
    "    results.append(rf_model.oob_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'auc score')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(tuned_n_estimators, results)\n",
    "plt.xlabel('n_estimators')\n",
    "plt.ylabel('auc score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "得分最大在n_estimators=280。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### max_depth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "max_depth调参建议范围：10-100，代码请写在下面的单元："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 280\n",
      "building tree 2 of 280building tree 3 of 280\n",
      "\n",
      "building tree 4 of 280\n",
      "building tree 5 of 280\n",
      "building tree 6 of 280building tree 7 of 280\n",
      "building tree 8 of 280\n",
      "\n",
      "building tree 9 of 280\n",
      "building tree 10 of 280\n",
      "building tree 11 of 280\n",
      "building tree 12 of 280\n",
      "building tree 13 of 280\n",
      "building tree 14 of 280\n",
      "building tree 15 of 280\n",
      "building tree 16 of 280\n",
      "building tree 17 of 280\n",
      "building tree 18 of 280\n",
      "building tree 19 of 280\n",
      "building tree 20 of 280\n",
      "building tree 21 of 280\n",
      "building tree 22 of 280\n",
      "building tree 23 of 280\n",
      "building tree 24 of 280\n",
      "building tree 25 of 280\n",
      "building tree 26 of 280\n",
      "building tree 27 of 280\n",
      "building tree 28 of 280\n",
      "building tree 29 of 280\n",
      "building tree 30 of 280\n",
      "building tree 31 of 280\n",
      "building tree 32 of 280\n",
      "building tree 33 of 280\n",
      "building tree 34 of 280\n",
      "building tree 35 of 280\n",
      "building tree 36 of 280\n",
      "building tree 37 of 280\n",
      "building tree 38 of 280\n",
      "building tree 39 of 280\n",
      "building tree 40 of 280\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    3.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 41 of 280\n",
      "building tree 42 of 280\n",
      "building tree 43 of 280\n",
      "building tree 44 of 280\n",
      "building tree 45 of 280\n",
      "building tree 46 of 280\n",
      "building tree 47 of 280\n",
      "building tree 48 of 280\n",
      "building tree 49 of 280\n",
      "building tree 50 of 280\n",
      "building tree 51 of 280\n",
      "building tree 52 of 280\n",
      "building tree 53 of 280\n",
      "building tree 54 of 280\n",
      "building tree 55 of 280\n",
      "building tree 56 of 280\n",
      "building tree 57 of 280\n",
      "building tree 58 of 280\n",
      "building tree 59 of 280\n",
      "building tree 60 of 280building tree 61 of 280\n",
      "\n",
      "building tree 62 of 280\n",
      "building tree 63 of 280\n",
      "building tree 64 of 280\n",
      "building tree 65 of 280\n",
      "building tree 66 of 280\n",
      "building tree 67 of 280\n",
      "building tree 68 of 280\n",
      "building tree 69 of 280\n",
      "building tree 70 of 280\n",
      "building tree 71 of 280\n",
      "building tree 72 of 280\n",
      "building tree 73 of 280\n",
      "building tree 74 of 280\n",
      "building tree 75 of 280\n",
      "building tree 76 of 280\n",
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      "[Parallel(n_jobs=8)]: Done 146 tasks      | elapsed:   14.9s\n"
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     ]
    },
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     "output_type": "stream",
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      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   28.0s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "building tree 1 of 280\n",
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    4.4s\n"
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    },
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     "output_type": "stream",
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      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   40.1s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "building tree 1 of 280\n",
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      "\n",
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    4.6s\n"
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      "building tree 39 of 280\n",
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      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   43.4s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
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      "building tree 1 of 280building tree 2 of 280\n",
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    4.7s\n"
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      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   42.8s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
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      "building tree 1 of 280building tree 2 of 280\n",
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    4.6s\n"
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      "[Parallel(n_jobs=8)]: Done 146 tasks      | elapsed:   23.2s\n"
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     ]
    },
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      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   43.3s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "building tree 1 of 280\n",
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      "\n",
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      "[Parallel(n_jobs=8)]: Done 146 tasks      | elapsed:   23.2s\n"
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    },
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      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   43.4s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
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     "output_type": "stream",
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      "building tree 1 of 280\n",
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    4.6s\n"
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    },
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      "[Parallel(n_jobs=8)]: Done 146 tasks      | elapsed:   25.0s\n"
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     ]
    },
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      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   45.9s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
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      "building tree 1 of 280building tree 2 of 280\n",
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    4.9s\n"
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      "[Parallel(n_jobs=8)]: Done 146 tasks      | elapsed:   24.5s\n"
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    },
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     ]
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      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   44.5s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "building tree 1 of 280\n",
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    },
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    4.7s\n"
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    },
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      "[Parallel(n_jobs=8)]: Done 146 tasks      | elapsed:   23.0s\n"
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    },
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      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   42.6s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
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    },
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     "output_type": "stream",
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      "building tree 1 of 280\n",
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    {
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     "output_type": "stream",
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    4.7s\n"
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    },
    {
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Done 146 tasks      | elapsed:   23.6s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   44.1s finished\n"
     ]
    }
   ],
   "source": [
    "tuned_max_depth = range(10, 110, 10)\n",
    "results = []\n",
    "params = {\n",
    "    'criterion': 'entropy',\n",
    "    'n_estimators': 280,\n",
    "    'max_features': 100,\n",
    "    'max_samples': 0.1,\n",
    "    'bootstrap': True,\n",
    "    'oob_score': True,\n",
    "    'class_weight': 'balanced',\n",
    "    'n_jobs': 8,\n",
    "    'random_state': 33,\n",
    "    'verbose': 2,\n",
    "}\n",
    "\n",
    "for max_depth in tuned_max_depth:\n",
    "    params['max_depth'] = max_depth\n",
    "    rf_model = RandomForestClassifier(**params)\n",
    "    rf_model.fit(feats_train, train_labels)\n",
    "    results.append(rf_model.oob_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(tuned_max_depth, results)\n",
    "plt.xlabel('tuned_max_depth')\n",
    "plt.ylabel('oob score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "得分最大在max_depth = 30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### min_samples_leaf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "min_samples_leaf调参建议范围：100-1000，代码请写在下面的单元："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 280\n",
      "building tree 2 of 280building tree 3 of 280\n",
      "building tree 4 of 280\n",
      "\n",
      "building tree 5 of 280building tree 6 of 280\n",
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      "\n",
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      "building tree 14 of 280building tree 15 of 280\n",
      "\n",
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      "building tree 37 of 280\n",
      "building tree 38 of 280\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    3.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 39 of 280building tree 40 of 280\n",
      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Done 146 tasks      | elapsed:   15.3s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "building tree 154 of 280\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   28.5s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 280\n",
      "building tree 2 of 280building tree 3 of 280building tree 4 of 280\n",
      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    2.6s\n"
     ]
    },
    {
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     "output_type": "stream",
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      "building tree 41 of 280\n",
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      "\n",
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    },
    {
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     "text": [
      "[Parallel(n_jobs=8)]: Done 146 tasks      | elapsed:   12.8s\n"
     ]
    },
    {
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     "output_type": "stream",
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      "building tree 156 of 280\n",
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      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   24.1s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 280building tree 2 of 280\n",
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      "\n",
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      "\n",
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      "\n",
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      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   22.1s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
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      "building tree 1 of 280\n",
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      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   21.6s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
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     "output_type": "stream",
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      "building tree 1 of 280\n",
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    2.1s\n"
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      "building tree 41 of 280\n",
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      "[Parallel(n_jobs=8)]: Done 146 tasks      | elapsed:   10.3s\n"
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     ]
    },
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     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   19.5s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "building tree 1 of 280\n",
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    2.0s\n"
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      "\n"
     ]
    },
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      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   18.3s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
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     "output_type": "stream",
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      "building tree 1 of 280building tree 2 of 280\n",
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    1.9s\n"
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     ]
    },
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     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   17.5s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "building tree 1 of 280\n",
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    1.7s\n"
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    },
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     "output_type": "stream",
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      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   15.9s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "building tree 1 of 280\n",
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      "\n",
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      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    1.6s\n"
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      "[Parallel(n_jobs=8)]: Done 146 tasks      | elapsed:    8.3s\n"
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      "\n",
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      "\n",
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    {
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     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   15.5s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
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      "building tree 1 of 280\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   14.8s finished\n"
     ]
    }
   ],
   "source": [
    "tuned_min_samples_leaf = range(100, 1100, 100)\n",
    "results = []\n",
    "params = {\n",
    "    'criterion': 'entropy',\n",
    "    'n_estimators': 280,\n",
    "    'max_features': 100,\n",
    "    'max_depth' : 30,\n",
    "    'max_samples': 0.1,\n",
    "    'bootstrap': True,\n",
    "    'oob_score': True,\n",
    "    'class_weight': 'balanced',\n",
    "    'n_jobs': 8,\n",
    "    'random_state': 33,\n",
    "    'verbose': 2,\n",
    "}\n",
    "\n",
    "for min_samples_leaf in tuned_min_samples_leaf:\n",
    "    params['min_samples_leaf'] = min_samples_leaf\n",
    "    rf_model = RandomForestClassifier(**params)\n",
    "    rf_model.fit(feats_train, train_labels)\n",
    "    results.append(rf_model.oob_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(tuned_min_samples_leaf, results)\n",
    "plt.xlabel('min_samples_leaf')\n",
    "plt.ylabel('oob score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最大得分在min_samples_leaf = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用最佳参数组合，训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "请在下面填写调参得到的最佳参数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 280\n",
      "building tree 2 of 280building tree 3 of 280\n",
      "building tree 4 of 280\n",
      "building tree 5 of 280\n",
      "\n",
      "building tree 6 of 280building tree 7 of 280\n",
      "building tree 8 of 280\n",
      "\n",
      "building tree 9 of 280\n",
      "building tree 10 of 280\n",
      "building tree 11 of 280\n",
      "building tree 12 of 280building tree 13 of 280\n",
      "\n",
      "building tree 14 of 280\n",
      "building tree 15 of 280\n",
      "building tree 16 of 280\n",
      "building tree 17 of 280\n",
      "building tree 18 of 280\n",
      "building tree 19 of 280\n",
      "building tree 20 of 280\n",
      "building tree 21 of 280\n",
      "building tree 22 of 280\n",
      "building tree 23 of 280\n",
      "building tree 24 of 280\n",
      "building tree 25 of 280\n",
      "building tree 26 of 280\n",
      "building tree 27 of 280\n",
      "building tree 28 of 280\n",
      "building tree 29 of 280\n",
      "building tree 30 of 280\n",
      "building tree 31 of 280\n",
      "building tree 32 of 280\n",
      "building tree 33 of 280\n",
      "building tree 34 of 280\n",
      "building tree 35 of 280building tree 36 of 280\n",
      "\n",
      "building tree 37 of 280\n",
      "building tree 38 of 280\n",
      "building tree 39 of 280\n",
      "building tree 40 of 280\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    3.2s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 41 of 280\n",
      "building tree 42 of 280\n",
      "building tree 43 of 280\n",
      "building tree 44 of 280\n",
      "building tree 45 of 280\n",
      "building tree 46 of 280\n",
      "building tree 47 of 280\n",
      "building tree 48 of 280\n",
      "building tree 49 of 280\n",
      "building tree 50 of 280\n",
      "building tree 51 of 280\n",
      "building tree 52 of 280\n",
      "building tree 53 of 280\n",
      "building tree 54 of 280\n",
      "building tree 55 of 280\n",
      "building tree 56 of 280\n",
      "building tree 57 of 280\n",
      "building tree 58 of 280\n",
      "building tree 59 of 280\n",
      "building tree 60 of 280\n",
      "building tree 61 of 280\n",
      "building tree 62 of 280\n",
      "building tree 63 of 280\n",
      "building tree 64 of 280\n",
      "building tree 65 of 280\n",
      "building tree 66 of 280building tree 67 of 280\n",
      "\n",
      "building tree 68 of 280\n",
      "building tree 69 of 280\n",
      "building tree 70 of 280\n",
      "building tree 71 of 280\n",
      "building tree 72 of 280\n",
      "building tree 73 of 280\n",
      "building tree 74 of 280\n",
      "building tree 75 of 280\n",
      "building tree 76 of 280\n",
      "building tree 77 of 280\n",
      "building tree 78 of 280\n",
      "building tree 79 of 280\n",
      "building tree 80 of 280\n",
      "building tree 81 of 280\n",
      "building tree 82 of 280\n",
      "building tree 83 of 280\n",
      "building tree 84 of 280\n",
      "building tree 85 of 280\n",
      "building tree 86 of 280\n",
      "building tree 87 of 280\n",
      "building tree 88 of 280\n",
      "building tree 89 of 280\n",
      "building tree 90 of 280building tree 91 of 280\n",
      "\n",
      "building tree 92 of 280\n",
      "building tree 93 of 280\n",
      "building tree 94 of 280\n",
      "building tree 95 of 280\n",
      "building tree 96 of 280\n",
      "building tree 97 of 280\n",
      "building tree 98 of 280\n",
      "building tree 99 of 280\n",
      "building tree 100 of 280\n",
      "building tree 101 of 280\n",
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      "building tree 108 of 280\n",
      "building tree 109 of 280\n",
      "building tree 110 of 280\n",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Done 146 tasks      | elapsed:   15.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:   28.6s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=8)]: Done 146 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=8)]: Done 280 out of 280 | elapsed:    0.2s finished\n"
     ]
    }
   ],
   "source": [
    "# 设置参数\n",
    "params = {\n",
    "    'criterion': 'entropy',\n",
    "    'n_estimators': 280,\n",
    "    'max_depth': 30,\n",
    "    'min_samples_leaf': 100 ,\n",
    "    'max_features': 100,\n",
    "    'max_samples': 0.1,\n",
    "    'bootstrap': True,\n",
    "    'oob_score': True,\n",
    "    'class_weight': \"balanced\",\n",
    "    'n_jobs': 8,\n",
    "    'random_state': 33,\n",
    "    'verbose': 2,\n",
    "}\n",
    "\n",
    "# 创建模型\n",
    "rf_model = RandomForestClassifier(**params)\n",
    "\n",
    "# 模型训练\n",
    "rf_model.fit(feats_train, train_labels)\n",
    "\n",
    "# 模型对测试集做预测\n",
    "y_pred_tuned = rf_model.predict_proba(feats_test)[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建提交结果格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "submit = df_test[['SK_ID_CURR']]\n",
    "submit['TARGET'] = y_pred_tuned\n",
    "\n",
    "submit.to_csv('rf_tuned_baseline.csv', index = False)"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "请截图模型结果提交Kaggle的最终得分（截图复制粘贴到本单元即可）：\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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